Assignment 4: Term Deposits Subscription Prediction

 

Care Bank ran a campaign for term-deposit subscriptions last year for its existing customers that showed a healthy conversion rate of over 12%. The bank is interested in a term deposit subscription because it gets good returns from a term deposit than a savings account as the customer is deprived of the rights to access the money prior to the maturity unless the customer is ready to compensate the bank. Banks can use that money to invest in other markets for better returns. Now, the bank is planning to launch a new campaign again but this time bank wants to utilize data available from previous campaigns, and also bank wants to automate this process with better target marketing to increase the success ratio with a minimal budget.

The objective of this project is to build a model that will help the marketing department, in the next campaign, to identify the customers who have a higher probability of subscribing to the term deposit. This will increase the success ratio while at the same time reduce the cost of the campaign.

Data Dictionary:

  1. age: Age of customer
  2. job: Type of job
  3. marital: Marital status of customer
  4. education: Education of customer
  5. default: has credit in default?
  6. housing: has housing loan?
  7. loan: has a personal loan?
  8. balance: balance in the account
  9. contact: contact communication type
  10. month: last contact month of the year
  11. day_of_week: last contact day of the week
  12. campaign: number of contacts performed during this campaign and for this client
  13. pdays: number of days that passed by after the client was last contacted from a previous campaign
  14. previous: number of contacts performed before this campaign and for this client
  15. poutcome: outcome of the previous marketing campaign
  16. Output variable- Target: has the client subscribed to a term deposit?
  17. duration: last contact duration, in seconds

Contents:

  1. Exploratory Data Analysis and Data Processing
  2. Recommendations

Exploratory Data Analysis and Insights:

Check formissing values:

Update Data Types:

EDA:

Exploring Univariate and Bivariate Analysis based on Job

Observations

Observations

Exploring Univariate and Bivariate Analysis based on Marital Status

Observations

Exploring Univariate and Bivariate Analysis based on Education Status

Observations

Exploring Univariate and Bivariate Analysis based on Housing Status

Exploring Univariate and Bivariate Analysis based on default Status

Exploring Univariate and Bivariate Analysis based on loan Status

Exploring Univariate and Bivariate Analysis based on contact Status

Exploring Univariate and Bivariate Analysis based on month Status

Exploring Univariate and Bivariate Analysis based on Target Status

Outlier Treatment

Feature Balance Outlier Treatment

Feature duration Outlier Treatment

Feature campaign Outlier Treatment

Feature previous Outlier Treatment

Skewed Data Treatment

Data Transformation and Feature Engineering

Drop Lower Corrollation Features

Model building

Model Perfomace Evaluation Helper Functions

Model building - Bagging:

Model building - Bagging Tuned:

Model building - Boosting:

Model building - Boosting Tuned:

Business Recommendations: